Big Data Management and Analytics (-)

Erasmus Universiteit Rotterdam (EUR)

Hier vind je de beste samenvattingen om te slagen voor Big Data Management and Analytics (-). Er zijn o.a. samenvattingen, aantekeningen en oefenvragen beschikbaar.

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Summary Big data management & Analytics. Grade: 8.8 Summary Big data management & Analytics. Grade: 8.8
  • Summary Big data management & Analytics. Grade: 8.8

  • Samenvatting • 84 pagina's • 2020
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  • Summary of the course BDMA. Grade achieved: 8.8
    (2)
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Complete Summary of all BDMA Lectures, Notes and a Practice Exam Complete Summary of all BDMA Lectures, Notes and a Practice Exam
  • Complete Summary of all BDMA Lectures, Notes and a Practice Exam

  • Samenvatting • 81 pagina's • 2019
  • Ook in voordeelbundel
  • A comprehensive summary of the lectures that were based on the book ‘Data Science for Business; What You Need To Know About Data Mining and Data-Analytic Thinking’ written by Foster Provost & Tom Fawcett. The summary includes screenshots of all key concepts (i.e. learning objectives) of the course Big Data Management & Analytics at RSM. I went to every lecture (i.e. Data Analytics, Data Collection and Ethics, Predictive Modeling, Model Fit, Overfit and Evaluation, Additional Data Science To...
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BDMA summary (literature   Lectures) BDMA summary (literature   Lectures)
  • BDMA summary (literature Lectures)

  • Samenvatting • 74 pagina's • 2018
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  • (Grade: 8,3) Summary BDMA 2017/2018 of the following materials: Table of Contents Week 1 Chapter 1: Introduction – Data-Analytical Thinking Lecture 1 Week 2 Chapter 2: Business Problems and Data Science Solutions Lecture 2: Collecting data for a data mining project Week 3 Chapter 3: Introduction to Predictive Modelling: From Correlation to Supervised Segmentation Chapter 4: Fitting a Model to Data Article: Predictive Analytics in Information Systems Research Lecture 3 – Predictive model...
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Summary Big Data and Analytics Summary Big Data and Analytics
  • Summary Big Data and Analytics

  • Samenvatting • 63 pagina's • 2017
  • MAIN BOOK: Provost & Fawcett (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking ADDITIONAL ARTICLES: Richard & King (2014). Big Data Ethics Marz and Warren (2015). Chapter 1: A new paradigm for Big Data
    (4)
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